Novel Insights into Elementary Girls' Experiences in Physiological Computing
Feiya Luo, Ruohan Liu, Idowu David Awoyemi, Chris S. Crawford, Fatema Nasrin · 2024
Previous research has incorporated physiological data such as heart rate and footsteps to enrich K-12 students' STEM and computing learning experiences. This qualitative study piloted a series of lessons leveraging a novel physiological computing environment with a small group of fifth-grade girls (n=5). The purpose of this study was to understand (1) how the physiological computing lesson activities promoted changes in the students' conceptual understanding of conditional logic and variables and their diverse perspectives in computing and (2) how the students approached problem-solving and what their visual attention looked like during physiological computing. We analyzed multiple sources of data, including students' artifacts, recorded classroom conversations, think-aloud verbalizations, and eye-tracking metrics data. Data analyses revealed that the girls demonstrated an improved understanding of the two computing concepts (i.e., variables and conditional logic), employed different problem-solving strategies, and encountered common challenges such as translating the task instruction to building code with the conditional block. Eye-tracking revealed that the students rarely attended to program output during their programming and debugging processes.